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Thinking Socratic

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tjboudreaux
thinking-socratic

When a request is vague, assumption-laden, or "obvious," ask the few load-bearing questions that expose hidden requirements before building or committing.

Overview

Publishertjboudreaux
Repositorycc-thinking-skills
Skill namethinking-socratic
Stars
1.3K
Forks
158
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by tjboudreaux on GitHub. Read the source before you install it.

Installation

Install the Thinking Socratic AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tjboudreaux/cc-thinking-skills.git /tmp/cc-thinking-skills
mkdir -p .claude/skills
cp -r /tmp/cc-thinking-skills/skills/thinking-socratic .claude/skills/thinking-socratic
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Thinking Socratic in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Thinking Socratic on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Thinking Socratic is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Socratic Questioning

Core rule: Surface the load-bearing assumption or undefined term before you build. Ask only what you cannot resolve yourself; stop when the next action is decision-ready.

When to Use

  • Request is underspecified ("make it fast", "add a dashboard", "fix the bug") and a guess would misbuild.
  • Claim rests on an unstated assumption that may be the real problem.
  • Someone treats a premise as "obvious" or jumps to a solution before the problem is defined.
  • Debugging a vague symptom that needs a checkable specific before investigation.

When NOT to Use

  • Spec is already clear and actionable — do the work; do not interrogate for theater.
  • Ambiguity is resolvable by reading code, running a command, or checking docs — resolve it yourself.
  • Mid-execution of an agreed plan — re-questioning every step is friction, not rigor.
  • Emergency where one load-bearing fact is enough to act — clarify that fact, then act (prefer ooda).
  • You need the strongest opposing case, not clarification — use steel-manning.
  • You need causal chain depth on a defined failure — use five-whys-plus or scientific-method.

Procedure

  1. Name the gap. State what is undefined, assumed, or uncheckable. If you can fill it from tools/repo without the user, do that and stop.
  2. Ask the load-bearing question first. Prefer one question whose answer most changes what you will build. Categories (use only what the gap needs):
    • Clarification — "What does X mean / for whom / success looks like?"
    • Assumption — "What must be true? What if it is false?"
    • Evidence — "What supports this? What would disprove it?"
    • Perspective — "Who is affected / who would disagree?"
    • Implication — "What follows if we do this?"
    • Meta — "Is this the right question?"
  3. Resolve or branch. From the answer, either (a) write the clarified requirement/decision and proceed, or (b) ask at most one follow-up that still gates the work. Do not run all six categories by default.
  4. Make assumptions explicit. Restate: "This assumes X; success means Y; out of scope is Z." Confirm only if still ambiguous after your restatement.
  5. Stop at decision-ready clarity. When the next action no longer depends on a hidden premise, end questioning and act or hand off. Cap user-facing questions tightly; prefer batching the few that truly gate work.

Output

text
gap: <what was vague or assumed>
resolved_by: self | user | mixed
questions_asked:
  - <only questions actually needed>
assumptions_made_explicit:
  - <X must be true / success = Y>
clarified_requirement: <decision-ready statement>
next_action: <build | investigate | re-scope | stop>
stop_reason: clear_enough | self_resolved | blocked_on_<fact>

Verification

  • Falsify / stop: If answers do not change the plan, you asked non-load-bearing questions — stop interrogating. If the "clarification" is still a guess, do not build; name the remaining blocker.
  • Over-application guard: Do not Socratic-interview a well-specified task. Do not outsource facts you can read or measure. Do not turn every step of execution into a new question round.

Frequently asked questions

What does the Thinking Socratic AI skill do?

When a request is vague, assumption-laden, or "obvious," ask the few load-bearing questions that expose hidden requirements before building or committing.

Why use Thinking Socratic on TypingMind?

Because you install it once and use it with any model. Thinking Socratic is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Thinking Socratic in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tjboudreaux/cc-thinking-skills/tree/main/skills/thinking-socratic. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Thinking Socratic?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Thinking Socratic?

As many as you like. As long as a model supports skills, you can use Thinking Socratic with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Thinking Socratic AI skill free?

Yes. It is published on GitHub by tjboudreaux under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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